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ORCIDFrank E. Curtis
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- affiliation: Lehigh University, Department of Industrial and Systems Engineering, Bethlehem, PA, USA
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2020 – today
- 2024
[j49]Frank E. Curtis
, Michael O'Neill, Daniel P. Robinson:
Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization. Math. Program. 205(1): 431-483 (2024)- [j48]Frank E. Curtis, Suyun Liu, Daniel P. Robinson:
Fair machine learning through constrained stochastic optimization and an ε-constraint method. Optim. Lett. 18(9): 1975-1991 (2024) - [j47]Gulcin Dinc Yalcin, Frank E. Curtis:
Incremental quasi-Newton algorithms for solving a nonconvex, nonsmooth, finite-sum optimization problem. Optim. Methods Softw. 39(2): 345-367 (2024) - [i11]Frank E. Curtis, Xin Jiang, Qi Wang:
Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimization. CoRR abs/2408.16186 (2024) - [i10]Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson:
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy through Single-Shot Federated Learning. CoRR abs/2409.09532 (2024) - 2023
- [j46]Ignacio Aravena, Daniel K. Molzahn, Shixuan Zhang, Cosmin G. Petra, Frank E. Curtis, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong, Amin Gholami, Kaizhao Sun, Xu Andy Sun, Stephen T. Elbert, Jesse Holzer, Arun Veeramany:
Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition. Oper. Res. 71(6): 1997-2014 (2023) - [j45]Frank E. Curtis, Daniel K. Molzahn, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong:
A Decomposition Algorithm with Fast Identification of Critical Contingencies for Large-Scale Security-Constrained AC-OPF. Oper. Res. 71(6): 2031-2044 (2023) - [j44]Man Yiu Tsang, Karmel S. Shehadeh, Frank E. Curtis:
An inexact column-and-constraint generation method to solve two-stage robust optimization problems. Oper. Res. Lett. 51(1): 92-98 (2023) - [j43]Frank E. Curtis, Qi Wang:
Worst-Case Complexity of TRACE with Inexact Subproblem Solutions for Nonconvex Smooth Optimization. SIAM J. Optim. 33(3): 2191-2221 (2023) - [c2]Yutong Dai, Guanyi Wang, Frank E. Curtis, Daniel P. Robinson:
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization. AISTATS 2023: 5107-5133 - [i9]Frank E. Curtis, Vyacheslav Kungurtsev, Daniel P. Robinson, Qi Wang:
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems. CoRR abs/2304.14907 (2023) - [i8]Frank E. Curtis, Xin Jiang, Qi Wang:
Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimization. CoRR abs/2308.03687 (2023) - 2022
- [j42]Frank E. Curtis, Minhan Li:
Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation. INFORMS J. Optim. 4(4): 426-445 (2022) - [j41]Albert S. Berahas, Frank E. Curtis, Baoyu Zhou:
Limited-memory BFGS with displacement aggregation. Math. Program. 194(1): 121-157 (2022) - [j40]Frank E. Curtis, Rui Shi:
A fully stochastic second-order trust region method. Optim. Methods Softw. 37(3): 844-877 (2022) - [j39]Frank E. Curtis, Yutong Dai, Daniel P. Robinson:
A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer. SIAM J. Optim. 32(2): 545-572 (2022) - 2021
- [j38]Frank E. Curtis, Daniel P. Robinson:
Regional complexity analysis of algorithms for nonconvex smooth optimization. Math. Program. 187(1): 579-615 (2021) - [j37]Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác:
An accelerated communication-efficient primal-dual optimization framework for structured machine learning. Optim. Methods Softw. 36(1): 20-44 (2021) - [j36]Frank E. Curtis, Daniel P. Robinson, Clément W. Royer, Stephen J. Wright:
Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization. SIAM J. Optim. 31(1): 518-544 (2021) - [j35]Albert S. Berahas, Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou:
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. SIAM J. Optim. 31(2): 1352-1379 (2021) - 2020
- [j34]Wenbo Gao, Donald Goldfarb, Frank E. Curtis:
ADMM for multiaffine constrained optimization. Optim. Methods Softw. 35(2): 257-303 (2020) - [j33]James V. Burke, Frank E. Curtis, Hao Wang, Jiashan Wang:
Inexact Sequential Quadratic Optimization with Penalty Parameter Updates within the QP Solver. SIAM J. Optim. 30(3): 1822-1849 (2020) - [j32]Frank E. Curtis, Katya Scheinberg:
Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms. IEEE Signal Process. Mag. 37(5): 32-42 (2020) - [i7]Frank E. Curtis, Katya Scheinberg:
Adaptive Stochastic Optimization. CoRR abs/2001.06699 (2020) - [i6]Albert S. Berahas, Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou:
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. CoRR abs/2007.10525 (2020)
2010 – 2019
- 2019
- [j31]Frank E. Curtis, Katya Scheinberg, Rui Shi:
A Stochastic Trust Region Algorithm Based on Careful Step Normalization. INFORMS J. Optim. 1(3): 200-220 (2019) - [j30]Frank E. Curtis, Daniel P. Robinson:
Exploiting negative curvature in deterministic and stochastic optimization. Math. Program. 176(1-2): 69-94 (2019) - 2018
- [j29]Frank E. Curtis, Zachary Lubberts, Daniel P. Robinson:
Concise complexity analyses for trust region methods. Optim. Lett. 12(8): 1713-1724 (2018) - [j28]Tianyi Chen, Frank E. Curtis, Daniel P. Robinson:
FaRSA for ℓ1-regularized convex optimization: local convergence and numerical experience. Optim. Methods Softw. 33(2): 396-415 (2018) - [j27]Frank E. Curtis, Andreas Wächter, Victor M. Zavala:
A Sequential Algorithm for Solving Nonlinear Optimization Problems with Chance Constraints. SIAM J. Optim. 28(1): 930-958 (2018) - [j26]Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi:
Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization. SIAM J. Optim. 28(2): 1533-1563 (2018) - [j25]Léon Bottou, Frank E. Curtis, Jorge Nocedal:
Optimization Methods for Large-Scale Machine Learning. SIAM Rev. 60(2): 223-311 (2018) - 2017
- [j24]Frank E. Curtis, Arvind U. Raghunathan:
Solving nearly-separable quadratic optimization problems as nonsmooth equations. Comput. Optim. Appl. 67(2): 317-360 (2017) - [j23]Frank E. Curtis, Nicholas I. M. Gould, Daniel P. Robinson, Philippe L. Toint:
An interior-point trust-funnel algorithm for nonlinear optimization. Math. Program. 161(1-2): 73-134 (2017) - [j22]Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi:
A trust region algorithm with a worst-case iteration complexity of O(ϵ -3/2) for nonconvex optimization. Math. Program. 162(1-2): 1-32 (2017) - [j21]Frank E. Curtis, Tim Mitchell, Michael L. Overton:
A BFGS-SQP method for nonsmooth, nonconvex, constrained optimization and its evaluation using relative minimization profiles. Optim. Methods Softw. 32(1): 148-181 (2017) - [j20]Tianyi Chen, Frank E. Curtis, Daniel P. Robinson:
A Reduced-Space Algorithm for Minimizing ℓ1-Regularized Convex Functions. SIAM J. Optim. 27(3): 1583-1610 (2017) - [i5]Frank E. Curtis, Katya Scheinberg:
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning. CoRR abs/1706.10207 (2017) - [i4]Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi:
Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization. CoRR abs/1707.00337 (2017) - [i3]Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác:
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning. CoRR abs/1711.05305 (2017) - 2016
- [j19]Frank E. Curtis, Nicholas I. M. Gould, Hao Jiang, Daniel P. Robinson:
Adaptive augmented Lagrangian methods: algorithms and practical numerical experience. Optim. Methods Softw. 31(1): 157-186 (2016) - [j18]Frank E. Curtis, Zheng Han:
Globally Convergent Primal-Dual Active-Set Methods with Inexact Subproblem Solves. SIAM J. Optim. 26(4): 2261-2283 (2016) - [c1]Frank E. Curtis:
A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization. ICML 2016: 632-641 - [i2]Léon Bottou, Frank E. Curtis, Jorge Nocedal:
Optimization Methods for Large-Scale Machine Learning. CoRR abs/1606.04838 (2016) - 2015
- [j17]Frank E. Curtis, Zheng Han, Daniel P. Robinson:
A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization. Comput. Optim. Appl. 60(2): 311-341 (2015) - [j16]Frank E. Curtis, Hao Jiang, Daniel P. Robinson:
An adaptive augmented Lagrangian method for large-scale constrained optimization. Math. Program. 152(1-2): 201-245 (2015) - [j15]Frank E. Curtis, Xiaocun Que:
A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees. Math. Program. Comput. 7(4): 399-428 (2015) - [j14]James V. Burke, Frank E. Curtis, Hao Wang, Jiashan Wang:
Iterative Reweighted Linear Least Squares for Exact Penalty Subproblems on Product Sets. SIAM J. Optim. 25(1): 261-294 (2015) - [i1]Zheng Han, Frank E. Curtis:
Primal-Dual Active-Set Methods for Isotonic Regression and Trend Filtering. CoRR abs/1508.02452 (2015) - 2014
- [j13]James V. Burke, Frank E. Curtis, Hao Wang:
A Sequential Quadratic Optimization Algorithm with Rapid Infeasibility Detection. SIAM J. Optim. 24(2): 839-872 (2014) - [j12]Frank E. Curtis, Travis C. Johnson, Daniel P. Robinson, Andreas Wächter:
An Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Optimization. SIAM J. Optim. 24(3): 1041-1074 (2014) - 2013
- [j11]Frank E. Curtis, Xiaocun Que:
An adaptive gradient sampling algorithm for non-smooth optimization. Optim. Methods Softw. 28(6): 1302-1324 (2013) - 2012
- [j10]Frank E. Curtis, Johannes Huber, Olaf Schenk, Andreas Wächter:
A note on the implementation of an interior-point algorithm for nonlinear optimization with inexact step computations. Math. Program. 136(1): 209-227 (2012) - [j9]Frank E. Curtis:
A penalty-interior-point algorithm for nonlinear constrained optimization. Math. Program. Comput. 4(2): 181-209 (2012) - [j8]Frank E. Curtis, Michael L. Overton:
A Sequential Quadratic Programming Algorithm for Nonconvex, Nonsmooth Constrained Optimization. SIAM J. Optim. 22(2): 474-500 (2012) - 2010
- [j7]Richard H. Byrd, Frank E. Curtis, Jorge Nocedal:
An inexact Newton method for nonconvex equality constrained optimization. Math. Program. 122(2): 273-299 (2010) - [j6]Richard H. Byrd, Frank E. Curtis, Jorge Nocedal:
Infeasibility Detection and SQP Methods for Nonlinear Optimization. SIAM J. Optim. 20(5): 2281-2299 (2010) - [j5]Frank E. Curtis, Olaf Schenk, Andreas Wächter:
An Interior-Point Algorithm for Large-Scale Nonlinear Optimization with Inexact Step Computations. SIAM J. Sci. Comput. 32(6): 3447-3475 (2010)
2000 – 2009
- 2009
- [j4]Frank E. Curtis, Jorge Nocedal, Andreas Wächter:
A Matrix-Free Algorithm for Equality Constrained Optimization Problems with Rank-Deficient Jacobians. SIAM J. Optim. 20(3): 1224-1249 (2009) - 2008
- [j3]Richard H. Byrd, Frank E. Curtis, Jorge Nocedal:
An Inexact SQP Method for Equality Constrained Optimization. SIAM J. Optim. 19(1): 351-369 (2008) - 2007
- [j2]Frank E. Curtis, Jorge Nocedal:
Steplength selection in interior-point methods for quadratic programming. Appl. Math. Lett. 20(5): 516-523 (2007) - 2004
- [j1]Frank E. Curtis, John Drew, Chi-Kwong Li, Daniel Pragel:
Central groupoids, central digraphs, and zero-one matrices A satisfying A2=J . J. Comb. Theory A 105(1): 35-50 (2004)
Coauthor Index
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